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In this paper, we extend the interference channel with degraded message sets model by introducing multiple secondaries. The new model allows for multiple concurrent transmissions in a wireless network by employing dirty paper and superposition coding. We apply the proposed scheme to the communication between nodes in a wireless mesh network (WMN). The cases of different WMN topologies are analyzed, and numerical results show that considerable capacity gains are achieved when using the proposed model with multiple secondaries.

This paper investigates an optimization of the conventional relay selection for multirelay environments. In contrast with previously reported selection schemes, where a selected relay accesses the channel in a dedicated cooperative slot, the proposed scheme recovers the bandwidth loss of the half-duplex constraint by allowing two relays to simultaneously access the channels. Based on an appropriate dirty-paper coding (DPC) technique among relays, the proposed scheme enables a relay to establish communication with the destination at the same time that another relay forwards the data from the source. It is proven that the interplay between relay selection and the superposition DPC weight factor provides a tradeoff between relaying and new data performance. Hence, an appropriate codesign of the superposition DPC parameter and opportunistic relay selection can achieve efficient communication for the new data without affecting the relaying performance. The proposed scheme is compared with conventional relaying approaches, and its enhancements are provided through theoretical studies and numerical results.

Multiple-input-multiple-output (MIMO) systems are of interest for meeting the expected demand for higher data rates and lower delays in future wireless packet data systems. In such systems, it is optimal to simultaneously transmit to multiple users compared with a single user in a single-input-single-output system. In addition, multicarrier systems are of interest to combat frequency-selective fading that is experienced over the larger bandwidth that these future broadband systems will use. The use of dirty paper coding further complicates the matter, because the order in which the users are encoded will affect the rates that they can achieve. A well-designed cross-layer scheduling algorithm must take into account the multiple dimensions of this resource-allocation problem and other quality-of-service (QoS) parameters to fully exploit the communications channel. The scheduling problem is often expressed in terms of optimizing some utility function. Unfortunately, the search space for this optimization problem is extremely large, which prohibits an optimal exhaustive search. To this end, we investigate the use of genetic algorithms to reduce the complexity of the scheduling. This paper builds upon prior work that implements scheduling via genetic algorithms in the context of single-carrier systems using zero-forcing beamforming (ZFB). In this paper, we investigate how the genetic algorithm can be adapted to account for the effect of encoding order on the scheduling and how the scheduling can be extended to a multicarrier system. In particular, we investigate the maximum throughput and proportionally fair scheduling criteria. We demonstrate that the performance of the genetic algorithm is near optimal compared with an exhaustive search at a greatly reduced computational complexity. Furthermore, in the case of a multicarrier orthogonal frequency-division multiplexing (OFDM) system, an increase in capacity is shown relative to the single-carrier case.

This paper investigates distributed MIMO precoding design to maximize the weighted sum rate in a heterogeneous network where the multiple input multiple output full-duplex (MIMO-FD) small cells reuse the downlink spectrum of the macro base-station (BS) to exchange backhaul information. The multi-antenna BS transmits signals to the cellular users using the dirty paper coding technique and the MIMO-FD small cells apply FD precoding structures to effectively balance the received signal, the self-interference (SI), and the co-channel interference (CCI). Since the optimization problem is shown to be nonconvex, obtaining the global optimum is challenging. A low-complexity solution with distributed implementation is introduced with proved convergence. By applying the successive convex approximation technique and the duality between the broadcast channel and the multiple access channel, the original nonconvex problem is decomposed into a sequence of convex subproblems, which can be solved analytically and separately at each small cell and macro BS with limited channel state information exchange. Simulation results confirm the convergence and demonstrate the benefits of the introduced algorithm. It is shown that the SI and CCI can be suppressed effectively with sufficient cancellation power and number of transmit antennas at the FD small cells.

In rollover prevention systems, a real-time lateral load transfer ratio (LTR) is typically computed to predict the likelihood of a vehicle to rollover and, hence, initiate rollover prevention measures. A traditional LTR largely relies on a lateral accelerometer signal to calculate rollover propensity. A new predictive LTR (PLTR) is developed in this paper, which utilizes a driver's steering input and several other sensor signals available from the vehicle's electronic stability control system. The new PLTR index can provide a time-advanced measure of rollover propensity and, therefore, offers significant benefits for closed-loop rollover prevention. Simulation results are presented using the industry-standard software CarSim to demonstrate the benefits of the new PLTR index. Experimental results of open-loop comparisons between LTR and PLTR indexes are presented, followed by experimental results on the closed-loop implementation of a PLTR-based rollover prevention system. The results in this paper document how a predictive rollover index can be developed and the advantages of such a system in rollover prevention.

This paper discusses interference precancellation in digital hierarchical broadcasting (HB). In particular, we present the principles and implementation of structured dirty paper coding (SDPC) that approaches the capacity limit of dirty paper coding in multilayer broadcasting. As an alternative to Tomlinson-Harashima precoding (THP), SDPC eliminates the significant performance loss suffered by THP in the low signal-to-noise ratio (SNR) regime due to the modulo operation. The key idea behind the SDPC scheme is the exploitation of the modulation structure of interference, thereby simplifying the demodulation process in hierarchical reception. We exemplify the SDPC technique by implementing an SDPC-based HB system on a real-time test bed. The experimental results show that SDPC delivers the performance of ldquosuperposition coding with successive interference cancellationrdquo without extra computation or memory requirements at the receiver side.

In this paper, we investigate the uplink transmission performance of low-power wide-area (LPWA) networks with regards to coexisting radio modules. We adopt long range (LoRa) radio technique as an example of the network of focus even though our analysis can be easily extended to other situations. We exploit a new topology to model the network, where the node locations of LoRa follow a Poisson cluster process (PCP) while other coexisting radio modules follow a Poisson point process (PPP). Unlike most of the performance analysis based on stochastic geometry, we take noise into consideration. More specifically, two models, with a fixed and a random number of active LoRa nodes in each cluster, respectively, are considered. To obtain insights, both the exact and simple approximated expressions for coverage probability are derived. Based on them, area spectral efficiency and energy efficiency are obtained. From our analysis, we show how the performance of LPWA networks can be enhanced through adjusting the density of LoRa nodes around each LoRa receiver. Moreover, the simulation results unveil that the optimal number of active LoRa nodes in each cluster exists to maximize the area spectral efficiency., Comment: This paper has been accepted by IEEE Trans. Vehicular Technology (TVT)

Most applications of underwater acoustic sensor networks (UASNs) rely upon reliable location information of sensor nodes. However, the harsh underwater environment makes localization more challenging as compared to terrestrial sensor networks. This paper is concerned with a localization issue for UASNs, subject to clock asynchronization and node mobility. A hybrid architecture including autonomous underwater vehicles (AUVs), active and passive sensor nodes is designed, where AUVs act as anchor nodes to provide localization information for sensor nodes. In order to eliminate the effect of asynchronous clocks and compensate the mobility of sensor nodes, an asynchronous localization algorithm with mobility prediction is provided for active and passive sensor nodes. Then, two localization optimization problems are formulated as minimizing the sum of all measurement errors. To solve the localization optimization problems, iterative least squares estimators are designed. In addition, the convergence analysis and Crame r–Rao lower bound for the localization errors are also provided. Finally, simulation results show that the proposed localization approach can reduce the localization time by comparing with the exhaustive search-based localization method. Meanwhile, the asynchronous algorithm in this paper can effectively eliminate the impact of the clock asynchronization and node mobility.

In electric and hybrid electric vehicles, series-connected battery packs are commonly used. Should the state-of-health (SOH) of one or several individual cells deteriorate, the entire battery pack is affected, reducing battery pack capacity, which in turn reduces the maximum distance able to be driven. In order to predict the SOH of the individual battery cells, this paper introduces the concept of effective battery capacitance. Effective capacitance is defined as the local slope of the voltage versus charge curve derived from a third-order polynomial relationship between these parameters. The location of maximum effective capacitance can be used as a means of identifying end-of-life and/or catastrophic failure of battery modules. Four different Toyota Prius battery packs were used in establishing this method in the proof of concept work. This paper presents a linear relationship between maximum effective capacitance and SOH; this relationship is confirmed with a larger dataset.

This paper proposes the use of an adaptive control of a hybrid electric vehicle with dual clutch transmission (HDCT). First, this paper shows mathematical equations for the nonlinear system. Then it presents the linearized model for the proposed system. The control objective of the model reference adaptive controller (MRAC) considered in this paper is to minimize fuel consumption and reduce torque interruption in a hybrid electric vehicle. The MRAC can be used to control the electric motor during changes in speed and gear, and the system can adapt to a model that simulates different driver patterns. The effects of using different model responses as input combinations are analyzed in an effort to exploit the over-actuation feature of the system, as is the sensitivity of the performance to various design factors. The simulation results for an HDCT demonstrate that the MRAC achieves reduced torque interruption and less vehicle jerk compared to the conventional method of operation.

Increasing safety and automation in transportation systems has led to the proliferation of radar and IEEE 802.11pbased dedicated short-range communication (DSRC) in vehicles. However, current implementations of vehicular radar devices are expensive, use a substantial amount of bandwidth, and are susceptible to multiple security risks. In this paper, we use the IEEE 802.11 orthogonal frequency-division multiplexing communications waveform, as found in IEEE 802.11a/g/p, to perform radar functions. In this paper, we present an approach that determines the mean-normalized channel energy from frequencydomain channel estimates and models it as a direct sinusoidal function of target range, enabling closest target range estimation. In addition, we propose an alternative to vehicular forward collision detection by extending IEEE 802.11 DSRC and WiFi technology to radar, extending the foundation ofjoint communications and radar frameworks. Furthermore, we perform an experimental demonstration near DSRC spectrum using IEEE 802.11 standard compliant software defined radios with potentially minimal modification through algorithm processing on frequency-domain channel estimates. The results of this paper show that our solution delivers sufficient accuracy and reliability for vehicular RADAR if we use the largest bandwidth available to IEEE 802.11p (20 MHz). This indicates significant potential for industrial devices with joint vehicular communications and radar capabilities.

Using a special type of sliding mode controller, a new type of traction control system (TCS) for hybrid four-wheel drive vehicles is developed. This paper makes two major contributions. First, a new electric powertrain architecture with an in-wheel motor at the front wheels and a clutch on the rear of the transmission is proposed for maximum traction force. The in-wheel motors are controlled to cycle near the optimal slip point. Based on the cycling patterns of the front wheels, the desired wheel speed for rear wheel is defined. The rear wheels are controlled to track this defined speed by controlling the clutch torque. Unlike conventional TCS algorithms, the proposed method exploits clutch control instead of brake control. Second, a special type of sliding mode controller which uses a nonlinear characteristic of the tire is proposed. An important distinction between the proposed sliding mode control method and other conventional feedback controllers is that the former does not depend on feedback error but provides the same functionality. Therefore, the practical aspects are emphasized in this paper. The developed method is confirmed in simulations, and the results reveal that the proposed method opens up opportunities for new types of TCS.

The papers in this special section focus on electric power trains, with particular emphasis on state-of-the-art research and development and new design concepts. These papers were presented at the IEEE Vehicle Power and Propulsion Conference (VPPC) that was held in Hangzhou, China, in October 2016.

This paper focuses on the secure transmission of wireless-powered full-duplex (FD) relay systems, where a multiantenna source communicates with a single-antenna destination with the help of a FD relay in the presence of a single-antenna eavesdropper. It is assumed that the FD relay is wireless energy harvesting-enabled, adopting both transmit and receive antennas to harvest energy in a time switching (TS) mode. As the objective of this paper is to maximize the system secrecy rate through jointly designing the energy beamforming vector, the information beamforming vector and the TS coefficient, an optimization problem is formulated. The formulated problem is proved to be nonconvex and the challenge to solve which is to concurrently solve out the three variables. To cope with this difficulty, an iterative algorithm is proposed to convert the formulated optimization problem into three convex subproblems, based on which the closed form solutions for the beamforming vectors are derived and the TS coefficient is obtained. Convergence property of the iterative method is analyzed. Simulations are also done to verify the theoretical derivations in terms of the convergence speed and the secrecy rate. Results reveal that the secrecy rate performance of exploiting transmit antenna together with receive antenna for energy harvesting at the FD relay outperforms only receive antenna case. Moreover, although there exists loopback interference between antennas, the FD relaying can always substantially boost the secrecy rate compared with half-duplex relaying architecture.

With the emergence of in-vehicle applications, providing the required computational capabilities is becoming a crucial problem. This paper proposes a framework named autonomous vehicular edge (AVE) for edge computing on the road, with the aim of increasing the computational capabilities of vehicles in a decentralized manner. By managing the idle computational resources on vehicles and using them efficiently, the proposed AVE framework can provide computation services in dynamic vehicular environments without requiring particular infrastructures to be deployed. Specifically, this paper introduces a workflow to support the autonomous organization of vehicular edges. Efficient job caching is proposed to better schedule jobs based on the information collected on neighboring vehicles, including GPS information. A scheduling algorithm based on ant colony optimization is designed to solve this job assignment problem. Extensive simulations are conducted, and the simulation results demonstrate the superiority of this approach over competing schemes in typical urban and highway scenarios.

As the conventional spatial modulation (SM) system lacks an efficient downlink multiple access scheme, this paper proposes a novel downlink nonorthogonal multiple access aided multiple-input multiple-output SM system with finite alphabet inputs. Mutual information (MI) is used to characterize the system's achievable spectral efficiency (SE). Because the MI lacks a closed-form formulation, in this paper we propose a computational-efficient lower bound to provide an approximation to the simulated MI. Besides, preprocessing is also designed at the receiver to mitigate the detrimental effect of channel correlation, which leads to striking a higher SE performance. Simulation results not only confirm the accuracy of our SE analysis, but also substantiate the superior SE performance achieved by the proposed preprocessing.

Both caching and interference alignment (IA) are promising techniques for next-generation wireless networks. Nevertheless, most of the existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this paper, we consider realistic time-varying channels. Specifically, the channel is formulated as a finite-state Markov channel (FSMC). The complexity of the system is very high when we consider realistic FSMC models. Therefore, in this paper, we propose a novel deep reinforcement learning approach, which is an advanced reinforcement learning algorithm that uses a deep $Q$ network to approximate the $Q$ value-action function. We use Google TensorFlow to implement deep reinforcement learning in this paper to obtain the optimal IA user selection policy in cache-enabled opportunistic IA wireless networks. Simulation results are presented to show that the performance of cache-enabled opportunistic IA networks in terms of the network's sum rate and energy efficiency can be significantly improved by using the proposed approach.

This paper investigates the energy consumption of distributed large-scale MIMO systems made up of a set of remote radio heads (RRHs), each of which is powered by both an independent energy harvesting source and the grid. The grid energy source allows to compensate for the randomness and intermittency of the harvested energy. Hence, the problem of grid power consumption minimization has to be solved by efficiently managing the energy delivered from different sources while satisfying the system requirements in terms of users’ quality of service demands. First, this paper solves the optimal offline version of the problem using linear programming. In fact, the main problem is decomposed and heuristically solved in order to deal with the large number of constraints and variables. Since sometimes all the users cannot be served, an iterative link removal algorithm is devised ensuring the feasibility of the problem. Next, we investigate the online energy management problem. We first propose a dynamic programming approach to obtain the optimal online solution but with high complexity. Then, we develop and propose a low complexity heuristic solution based on using the maximal available energy at the batteries. Taking benefits of the characteristics of large-scale MIMO and the modeling of the harvested energy as Markov chains, we devise an efficient online energy management algorithm based on energy prediction. Finally, we propose a heuristic solution for RRH on/off operation with the objective of improving the system energy efficiency. The performance of the proposed algorithms is evaluated by simulations and it is shown that the proposed energy management approaches offer efficient use of nonrenewable energy to compensate the variability of renewable energy in large-scale MIMO systems.

Existing research on a simultaneous wireless information and power transfer (SWIPT) system is based on the assumption of Gaussian inputs. However, the optimal design on Gaussian inputs may lead to dramatic performance loss for practical systems with finite-alphabet inputs. This paper focuses on the precoder design for a SWIPT system with finite-alphabet inputs and instantaneous channel state information over a multiple-input multiple-output channel. We formulate the optimal precoder design as an optimization problem, in which the objective is to maximize the mutual information over the channel from the transmitter to the information receiver under the constraints of transmit power and harvested energy threshold. The formulated problem is NP-hard, so a global optimal solution cannot be found within the polynomial time. The main contributions of this paper are as follows: 1) By using its structure, the NP-hard problem is relaxed to a semidefinite programming problem. Then, a general solving framework for both co-located and separated receiver cases, based on the semidefinite relaxation (SDR) technique, is developed to achieve a near optimal precoder. 2) For the case of co-located receivers, we show that the optimal precoder design is a concave problem with respect to power allocation; then, a specific algorithm for co-located receivers is proposed. Compared with the general SDR-based method, the specific algorithm for co-located receivers exhibits almost the same performance but much lower complexity. 3) The performance of several practical co-located receiver designs is analyzed in SWIPT systems with finite-alphabet inputs. Finally, we provide simulation results to show the efficacy of the proposed algorithms.

The series–parallel powertrains for high-power vehicles have complex architectures. They generally require multiple planetary gear trains with several clutches. The maintenance and durability of the clutches are nonetheless not relevant for on-board implementation. In this paper, a new architecture with a Ravigneaux gear train without clutches is proposed. The complexity of the architecture is then reduced. The objective of this paper is to illustrate the benefits of such a powertrain in terms of energy savings in comparison to a conventional truck. Due to the mechanical constraints of the gear train, the control scheme of the vehicle is complex to design. A functional description, the Energetic Macroscopic Representation, is then used to organize the control scheme. Simulation results, with a rules-based energy management strategy, show a reduction of the fuel consumption from 6% to 19% depending on the driving cycles. To prove the use of a Ravigneaux gear train as a power split device, the basic operating modes are validated in the open-loop control on a test bed.

This paper deals with the secure communication issues of wireless cooperative networks in the presence of multiple friendly but selfish intermediate nodes. On account of the broadcast nature of wireless communications, it is assailable to malicious eavesdropping. To tackle this challenge in the paper, we present a relay and jammer selection strategy that selects the jammer and relay nodes from intermediate nodes to improve the security of the eavesdropping attacks. The jammer is used to broadcast artificial interference noise on the eavesdropper. The relay acts as a traditional relay that retransmits source signal from the source to the intended destination. For achieving the maximum secrecy capacity by the selected nodes, we introduce a power allocation approach of intermediate nodes that is formulated as a Bertrand game based on price competition. We prove that an optimal pricing scheme can maximize the secrecy capacity and achieve the optimal profits for the selfish friendly nodes. Then, a new particle swarm with simulated anneal optimization algorithm is employed to obtain the solution of pricing and node selection. Finally, the simulation results verify our theoretical analysis.

This paper presents an application of dynamically driven recurrent networks (DDRNs) in online electric vehicle (EV) battery analysis. In this paper, a nonlinear autoregressive with exogenous inputs (NARX) architecture of the DDRN is designed for both state of charge (SOC) and state of health (SOH) estimation. Unlike other techniques, this estimation strategy is subject to the global feedback theorem (GFT) which increases both computational intelligence and robustness while maintaining reasonable simplicity. The proposed technique requires no model or knowledge of battery's internal parameters, but rather uses the battery's voltage, charge/discharge currents, and ambient temperature variations to accurately estimate battery's SOC and SOH simultaneously. The presented method is evaluated experimentally using two different batteries namely lithium iron phosphate ( $\text{LiFePO}_4$ ) and lithium titanate ( $\text{LTO}$ ) both subject to dynamic charge and discharge current profiles and change in ambient temperature. Results highlight the robustness of this method to battery's nonlinear dynamic nature, hysteresis, aging, dynamic current profile, and parametric uncertainties. The simplicity and robustness of this method make it suitable and effective for EVs’ battery management system (BMS).

Applications of unmanned aircraft systems (UAS) are expected to expand dramatically in the coming decades. In order to provide safe and reliable links for UAS, NASA Glenn Research Center sponsored a measurement campaign in 2013 to characterize air-ground (AG) channels. In the first three of our AG channel characterization series papers, path loss, small scale fading, stationarity distance, root mean square (RMS) delay spread, spatial correlations, and tapped delay line models were presented for a range of ground site environment. In this paper, we expand the results to a unique and little-studied characteristic of the AG channels—airframe shadowing—for a medium-sized aircraft. Airframe shadowing occurs when the line-of-sight signal is obstructed by the airplane itself in some specific maneuvers. Based on over 200 aircraft wing/engine shadowing events in both C-band (5060 MHz) and L-band (968 MHz) collected in the measurement campaign, shadowing depth, duration, multiple antenna diversity gain, and small scale fading results and their statistics are reported. Algorithms are proposed to simulate a random shadowing event based upon shadowing depth and duration cumulative density functions. Airframe shadowing characteristics are independent of the local ground site conditions and link distance. The shadowing loss is modeled as a function of aircraft roll angle, but shadowing loss and duration are essentially uncorrelated. The maximum shadowing loss measured was over 35 dB and the duration was up to 74 s. As expected, deployment of multiple aircraft antennas is shown helpful to mitigate this shadowing.

Diversity is used to combat fading and shadowing in narrowband spectrum sensing. In this paper, we propose two new detection algorithms for wideband spectrum sensing that use square law combining (SLC) diversity to improve the detection performance. We analyze the performance of these algorithms under Nakagami fading. The proposed schemes include channel by channel square law combining (CC-SLC) and ranked square law combining (R-SLC). We first provide the asymptotic analysis for decision statistic of received energy to simplify the analysis. The approximated probability density function (pdf) to decision statistic is used to derive the pdf of received energy for $P$ number of diversity branches. The performance of the proposed algorithms is measured in terms of probability of insufficient spectrum opportunity ( $P_{\text{ISO}}$ ) and probability of excessive interference opportunity ( $P_{\text{EIO}}$ ). The analysis provided in this paper is general and can be used with any fading model and diversity scheme. Experiments are carried out using theoretical analysis and also verified by using Monte-Carlo simulation. Our analysis shows that the proposed algorithms using diversity outperform the channel by channel and ranked channel detection algorithms used under no diversity. Our results also indicate that among the proposed detection algorithms R-SLC performs better.

In electric vehicles (EVs), the source of noise, vibration, and harshness (NVH) involves various components inside/outside of the vehicle. Besides the vibration from each NVH source, the resonances between multiple sources contribute even more significant vibrations. This paper proposes a generalized real-time suppression strategy to mitigate the coupling vibration during vehicle idling and cruising. Using a high-performance field-oriented-controlled electric motor drive, a relatively small amount of rotation velocity offset can be added to the traction motor to significantly reduce the resonant vibration with negligible impact to vehicular speed. In this paper, torsional and bending coupling vibrations between traction motor and driveline are investigated because these two are the most common forced vibrations due to resonances. The system is simulated using MATLAB/Simulink, and the results of the vibration suppression are presented. The strategy proposed in this paper presents a simple and robust way to suppress the coupling vibration in EVs with no additional hardware requirement.

The proposed solutions in the literature for integrating Device-to-Device (D2D) communication in cellular networks require added functionalities and consume valuable network resources, mainly in the discovery process. Unlike existing solutions, this paper mitigates the requirement of additional resources in the LTE-A network. This is achieved by proposing to offload a portion of the discovery traffic and processing of D2D communications that involve vehicular users (drivers and passengers) into Vehicular Ad-hoc Networks (VANETs) by using the inherent knowledge of the Road Side Units (RSUs) about users in their coverage areas. In addition, the paper develops an analytical model to analyze the duration of peer discovery in highway scenarios. The results are validated through simulation experiments using both the Network Simulator NS3 and Matlab. The analytical and numerical results demonstrate the effectiveness of the proposed scheme, and show that a low discovery latency is obtained.

Radio tomographic imaging systems use received signal strength measurements between static wireless sensors to image the changes in the radio propagation environment in the area of the sensors, which can be used to localize a person causing the change. To date, spatial models used for such systems are set a priori and do not change. Imaging and tracking performance suffers because of the mismatch between the model and the measurements. Collecting labeled training data requires intensive effort, and the data degrade quickly as the environment changes. This paper provides a means for a radio tomographic imaging system to bootstrap to improve its spatial models using unlabeled data, iteratively improving itself over time. A collection of tracking filters are presented to improve the accuracy of image and coordinate estimates. This paper presents an online method to use these estimates to instantaneously update spatial model parameters. Further, a smoothing method is presented to fine-tune the model with a given finite latency. The development efforts are evaluated using simulations and validated with real-world experiments conducted in three different environments. With respect to another state-of-the-art radio tomographic imaging system, the results suggest that the presented system increases the median tracking accuracy by twofold in the most challenging environment and by threefold when the model parameters are trained using the smoothing method.

This paper reconsiders the design of blind interference alignment (BIA) for $K$ -user MISO interference channel in which each receiver is equipped with a reconfigurable antenna of preset $M_k\geq 3$ modes. Beyond the degree of freedom (DoF)-oriented BIA (D-BIA) design proposed by Lu et al. , we further take into account the frequency of mode switching within one supersymbol frame. Even though every receiving antenna is reconfigurable with preset multiple modes, from an engineering perspective, it is of practical importance for each receiving antenna to make less number of mode switching in order to gain the benefits such as less switching power consumption and less signal distortion. To this end, we propose in this paper switching-oriented BIA (S-BIA) scheme, which preserves the same degree of freedom (DoF) gain as D-BIA while offering reduced mode switching frequency. Theoretical analysis and simulation results demonstrate that, compared to D-BIA, S-BIA can gain $100/(M-1)$ percent of switching saving when $M_k = M$ is odd for all transmitters. Remarkably, S-BIA offers 50% switching saving for multiuser $3\times 1$ interference channels.

With the ever-growing number of mobile users and the rapid growth of wireless data service requirement, quality of experience (QoE) has emerged as an essential indicator for users, service providers, and operators. Meanwhile, to improve coverage and serve users, a lot of small cell base stations (SBSs) must be installed, and a great amount of energy is consumed. However, as far as is known, there are few works that have studied the combinatorial problem of QoE and energy aware SBS management, which jointly implements power selection, load management (SU allocation), and channel allocation. This paper investigates the problem of QoE and energy aware SBS management, which consists of power selection, load management, and channel allocation. In this paper, we resort to cloud technologies to solve such a complicated combinatorial problem and employ an iterative approach in which two subproblems are alternatively assigned and optimized at each iteration, i.e., 1) transmission power and load joint management and 2) channel allocation. We propose a two-dimensional-action extended weakly acyclic game theoretical scheme to optimize the two subproblems distributedly and iteratively. We define a novel two-dimensional-action pure strategy Nash equilibrium (2D-NE) and prove that at least one 2D-NE exists in the proposed game. With the help of cloud, we propose two kinds of better response algorithms to achieve 2D-NE of the proposed game $G_w$ . Moreover, simulation results show that the proposed approach could achieve a good QoE-energy utility performance and a high QoE energy efficiency.

In cognitive radio (CR) networks, cooperative spectrum sensing is utilized to improve the sensing performance to avoid potential interference to primary users (PUs) and increase spectrum access opportunities for secondary users (SUs). A cooperative spectrum sensing process is divided into three phases: individual sensing/detection, reporting/fusion, and data transmission. In the reporting phase, one or more reporting channels are needed to transmit individual sensing results to a fusion center (FC), and global spectrum sensing results are determined at the FC. The number of required reporting channels depends on the number of spectrum sensors or SUs, which relates to reporting channel efficiency and channel scheduling complexity. That is to say, the reporting channel design can be a challenge, especially when fixed assignment scheduling is used. Therefore, in this paper, we design a reporting channel scheme based on random access protocols, including slotted Aloha and reservation-Aloha. Performance evaluations in terms of PU detection probabilities and false alarm probabilities considering the proposed reporting channels are presented. In addition, the impact of soft/unquantized spectrum sensors or detectors (SUs) and malicious SUs is considered in this paper. Analytical and simulation results illustrate the effectiveness of the proposed reporting channel scheduling methods in improving the cooperative spectrum sensing performance.

With the never-ending increase in the number of mobile connected devices and the need for higher data rates anywhere, anytime, higher frequency bands are being considered for communications. As millimeter-wave technology moves from research to commercial deployments, and motivated by the still limited bandwidth, the terahertz (THz) band is envisioned as the next frontier for communications. When it comes to vehicular networks, communication at much higher frequencies and, consequently, with much higher data rates brings many exciting opportunities as well as challenges. In this paper, an overview of the opportunities and challenges in THz communications for vehicular networks is provided. In addition, the papers in this Special Section which provide first-time solutions to some of these challenges, are introduced.

In this paper, we consider a heterogenous network in which low-power indoor femtocells are deployed in the coverage area of the existing macrobase station (MBS). This paper proposes a novel coordinated random beamforming and user-scheduling strategy to improve the throughput of users served by the femtocell access point (FAP) while satisfying the quality-of-service (QoS) requirements of users served by both MBS and FAP. The strategy, termed as QoS-aware coordinated scheduling (QACS), requires limited coordination between the MBS and FAP, i.e., only the indexes of the qualified beams are shared. Exact statistical analysis for the ergodic achievable rate of both FAP and MBS with the proposed strategy are presented. Scheduling fairness is also addressed for the proposed QACS.

The ever-increasing demand for mobile Internet and high-data-rate applications poses unique challenging requirements for 5G mobile networks, including spectrum limitations and massive connectivity. Cognitive radio and carrier aggregation (CA) have recently been proposed as promising technologies to overcome these challenges. In this paper, we investigate joint relay selection and optimal power allocation in an underlay cooperative cognitive radio with CA, taking into account the availability of multiple carrier components in two frequency bands, subject to outage probability requirements for primary users (PUs). The secondary user network employs relay selection, where the relay that maximizes the end-to-end sum rate is selected, assuming both decode-and-forward and amplify-and-forward relaying. The resulting optimization problems are optimally solved using convex optimization tools, i.e., dual decomposition and an efficient iterative method, allowing their application in practical implementations. Simulation results illustrate that the proposed configuration exploits the available degrees of freedom efficiently to maximize the SU rate, while meeting the PU average outage probability constraints. 1 1967-2012 IEEE. Manuscript received May 27, 2016; revised October 2, 2016; accepted November 7, 2016. Date of publication December 2, 2016; date of current version July 14, 2017. The work of P. D. Diamantoulakis, G. K. Karagiannidis, and T. Khattab was supported by the NPRP under Grant NPRP 6-1326-2-532 from the Qatar National Research Fund (a member of Qatar Foundation). This work was presented in part at the IEEE Wireless Communications and Networking Conference (WCNC), Doha, Qatar, Apr. 2016. The review of this paper was coordinated by Prof. D. B. da Costa. Scopus

This paper studies wireless charging of lithium-ion batteries for electric vehicles. The charging profile mandates a constant-current (CC) charging for a discharged battery until the battery voltage reaches the cutoff voltage at rated power. The charging continues at the cutoff voltage with a constant-voltage (CV) charging at a power level down to 3% of the rated power in order to fully charge the battery. An inductive-power-transfer (IPT) converter should be designed with minimal number of stages to achieve high efficiency. However, high efficiency for such a wide load range is difficult to achieve. Moreover, the efficiency-to-load relationship is distinctly different for CC and CV charging operations, posing difficulties for the single-stage design. This paper describes the design of a single-stage IPT converter that complies with the battery charging profile and, at the same time, achieves optimal efficiency. Design optimization includes soft switching for the entire battery load range, efficiency optimization for CC and CV modes of operation, and system efficiency optimization for the whole battery charging profile. Measured results of two experimental IPT battery chargers are presented for illustration and verification.

With the proliferation of sensors, such as accelerometers, in mobile devices, activity and motion tracking has become a viable technology to understand and create an engaging user experience. This paper proposes a fast adaptation and learning scheme of activity tracking policies when user statistics are unknown a priori, varying with time, and inconsistent for different users. In our stochastic optimization, user activities are required to be synchronized with a backend under a cellular data limit to avoid overcharges from cellular operators. The mobile device is charged intermittently using wireless or wired charging for receiving the required energy for transmission and sensing operations. Firstly, we propose an activity tracking policy by formulating a stochastic optimization as a constrained Markov decision process (CMDP). Secondly, we prove that the optimal policy of the CMDP has a threshold structure using a Lagrangian relaxation approach and the submodularity concept. We accordingly present a fast Q-learning algorithm by considering the policy structure to improve the convergence speed over that of conventional Q-learning. Finally, simulation examples are presented to support the theoretical findings of this paper., 14 pages, 10 figure

Fourth/Fifth Generation heterogeneous wireless networks (4G/5G HetNets) use or will use small cells (SCs) to extend network coverage and increase spectrum efficiency. However, the standard and technical specifications do not specify how to plan the locations of the SCs within the network. Several papers introduced strategies for planning the locations of SCs in the 4G HetNet architecture. However, SCs placement strategies to support the self-healing functionality of the 4G/5G self organizing networks framework has not been studied in the literature. The placement of SCs in 4G HetNets such that an SC failure will not interrupt service, hence making the network fault tolerant, is an important design and planning problem that is addressed in this paper. We present an integer linear program formulation for planning operators of managed SC locations with fault tolerance. We allow one SC to fail and by using self-healing, a fault-tolerance service is provided at designated fail-over levels (defined in terms of users throughput). We consider the problem of SC location planning by using offloading in both out-band and in-band modes, and an interference model is presented to consider the in-band mode and to address the effect of interference on SCs placement planning. A novel approach to provide a linear interference model by using an expanded state space to get rid of nonlinearity is introduced. We present numerical results that show how our model can be used to plan the positions of SCs. We also incorporate the existence of obstacles in the planning, such as large structures or natural formations, that might happen in real life. To the best of our knowledge, this is the first work that addresses the planning of SC locations in 4G/5G HetNets in a fault-tolerant manner.

In this paper, we present a novel Wi-Fi-based pedestrian collision avoidance (PCA) system: WiSafe. Compared to vision-based and radar-based PCA systems, WiSafe has a key advantage of Non-Line-of-Sight (NLOS). WiSafe can assist drivers in discovering pedestrians in NLOS and blind spots, where the views of drivers are blocked by buildings, vehicles, or other obstacles. In particular, it is very useful when driving in poor visibility conditions such as at night, in heavy rain, or thick fog. WiSafe can save split seconds, which can potentially save lives. Instead of using wireless access in vehicular environments/dedicated short-range communications to protect pedestrians, WiSafe provides a simple, effective, and low-cost solution. Without changing Wi-Fi standards, WiSafe is compatible with 802.11a/b/g/n and can be implemented by apps on smartphones or wearable devices. The complete analysis, implementation, and experiments are presented in this paper. The results show that WiSafe can achieve and even exceed the PCA requirements.

In this paper, a new cooperative two-way cognitive relaying scheme is proposed. In this model, a primary user (PU) network consisting of two PU sources communicate with each other via a single amplify-and-forward (AF) relay. In addition, a secondary user (SU) source transmits its data to an SU destination via the same PU relay node. To mitigate the SU interference caused to the PU network, the PU network considers the SU network pairs as two additional relay nodes helping the original PU relay node in improving the PU network performance. As a reward for its cooperation, the PU network allows the SU network to communicate simultaneously via the PU relay node by using the decode-and-forward protocol. The proposed system allows the transmission of four PU symbols and one SU symbol in four/three time slots resulting in a bandwidth efficiency of $1.25/1.67$ based on the cooperative scheme, respectively. Two power allocation optimization problems are formulated: the first problem minimizes the weighted sum of the average symbol error probability of both PU and SU systems, whereas the second problem maximizes the total achievable sum rate of the PU and SU networks. Lagrangian multiplier method is used to find the optimal solutions for both problems under the constraint of maximum allowable power budget. In addition, the paper investigates how the proposed model improves the PU physical layer security performance against a single passive eavesdropper. The results show that the error performance of the proposed relay selection model outperforms the conventional two-way relaying networks with AF protocol. Moreover, findings illustrate that the total achievable sum rate of the proposed relay elimination model is higher than the total achievable rate of the conventional two-way relaying model. From secrecy point of view, the proposed model is shown to achieve a nonzero secrecy rate that improves the PU system security against eavesdropping attacks.

In this paper, we propose a novel distributed cooperative sensing algorithm for connected vehicles. The adaptive energy detection threshold, which is used to decide whether the channel is busy, is optimized in this paper by using a computationally efficient numerical approach. The proposed optimization approach minimizes the probability of an incorrect detection as a function of both the probability of false alarm and the probability of missed detection. By considering both probabilities when computing the probability of incorrect detection, to the best of the authors' knowledge, the proposed approach is novel. During the energy detection process, large-scale fading, multipath fading, Doppler effect, and transmission errors affecting the control messaging process are accounted for in order to provide a practical solution for connected vehicles. Once the available channels have been detected, the vehicles share this information using broadcast control messages. Each vehicle evaluates the available channels by voting on the information received from one-hop neighbors, where the credibility of each neighbor is weighted during the voting process. An interdisciplinary approach referred to as entropy-based weighting is used to define the neighbor, as well as the vehicle's own credibility. The voting mechanism is switched between the proposed voting mechanism and the traditional voting approach obtained from the current state of the art in order to maintain a balance between the computational cost/latency and robust spectrum sensing. Experimental results show that by using the proposed distributed cooperative spectrum sensing mechanism, spectrum detection error converges to zero.

Contrary to the traditional theoretical analysis with perfect self-interference cancellation, this paper studies the outage performance of a full-duplex (FD) decode-and-forward two-way relay system with the consideration of the residual self-interference incurred by the FD mode. The exact closed-form outage probability expressions are derived in this paper. In addition, the asymptotic outage performance is investigated when the transmit powers increase to infinity, and the result demonstrates that the outage probabilities monotonically increase and finally converge to 1 as the transmit power exceeds a certain threshold. Furthermore, optimal power allocation schemes and optimal relay node placement strategies are then proposed to improve the outage performance. Finally, simulation results are conducted to verify the accuracy of the theoretical analysis.

This paper considers both nondistributed and distributed multilevel amplify-and-forward (AF) wireless relay networks. For a fair comparison, the nondistributed multilevel AF relay network consists of a one-source–one-destination pair and a relay at each level with multiple antennas, whereas the distributed network consists of a one-source–one-destination pair and multiple distributed relays at each level with a single antenna at each relay. In this paper, relay beamforming matrices are determined for both networks based on the minimum mean square error (MMSE) criterion. During data transmission, power is independently and individually constrained at the relays at each level. Finally, an iterative algorithm is proposed to solve the desired optimization problem.

Cellular heterogeneous networks (HetNets), which are realized through shared carrier deployments of macrocells and small cells, are widely seen as a potential solution to serve growing data capacity demands. While these networks pose significant challenges in interference management, much of the focus in earlier efforts has been limited only to small-cell range expansion in the downlink. In this paper, we provide a comprehensive analysis of interference management techniques applicable to the uplink of cellular HetNets, with the goal of characterizing and improving edge user (e.g., 5th percentile) performance. In particular, we propose and study virtual soft-handoff (VSHO), which offers a low-complexity and low-overhead solution that exploits the link asymmetry observed in the uplink geometry of HetNets. VSHO exploits the fact that, in the uplink, an edge macro user who suffers from very low throughput may be closer to a nearby small cell than its own macrocell. The intuition is that such macro users should have better decoding opportunity when their signals are processed at the nearby small cell with better channel conditions. We also study how interference cancelation (IC) and interference avoidance through resource blanking can be incorporated into our VSHO proposal for further throughput gains. In addition to the VSHO techniques outlined in this paper, we also analyze other candidate techniques available in the Third Generation Partnership Project Long-Term Evolution (3GPP-LTE), such as cell-range expansion (CRE) and joint processing, to present a unified view of interference management options available in HetNets.

A compact and efficient design for the electromagnetic interference (EMI) filter stage has become one of the most critical challenges in designing a high-density power converter, particularly for avionic applications. To maintain the regulatory standard requirements, EMI filter design needs to be precisely implemented. However, the attenuation characteristics of common-mode (CM) and differential-mode (DM) EMI filters may vary in practice from their respective theoretical calculations due to difficulty and inaccuracy in the noise modeling. This is particularly true for a three-phase active boost rectifier, which inherently has a CM high-frequency component in the rectifier output. This paper puts forward a detailed mathematical modeling of CM noise, originated from different sources, and discusses a concept to significantly reduce the CM noise effects. Furthermore, minimizing the weight and size of DM filter stage has been a design concern as the DM stage consumes a significant part of the weight of the whole system. This paper proposes a compact EMI filter topology with enhanced power density, which aims at satisfying the conducted EMI requirements according to DO-160F in avionics applications. The experimental verifications of the designed EMI filter are performed from the conducted emission results, obtained from a 6-kW laboratory prototype of an integrated stage of EMI and three-phase boost PFC converter.

In this paper, a spectrum-band selection scheme is developed for secondary users (SUs) in underlay spectrum-sharing networks to balance the performance between SUs and primary users (PUs) while guaranteeing PUs' priority. With the developed scheme, among multiple candidate spectrum bands, a given secondary transmitter (ST) is scheduled to access the spectrum band with minimum ratio of the ST–PU channel power gain to the SU link channel power gain. To evaluate this scheme, the SU's performance is investigated concerning its service delay. Under the constraint on average interference from the ST to PUs, the ST's optimal power allocation is obtained in a closed form to achieve the maximum effective capacity (EC) over SU links. Illustrative numerical results and asymptotic discussions substantiate the validity of our derivations and, moreover, demonstrate that, for the case of stringent delay requirements but loose average interference constraint, the band-selection scheme developed in this paper achieves better performance than both the selection of the band with maximum SU link channel power gain and that with minimum ST–PU channel power gain.

This paper investigates the distributed adaptive control problems for nonlinear vehicle-following systems subject to nonlinear acceleration uncertainties involving vehicle acceleration disturbances, wind gusts, and parameter uncertainties. It is worth mentioning that the acceleration of the leader in most existing studies is always assumed to be zero or constant; the evolution of the leader in this paper may be subject to some unknown bounded input. Distributed adaptive control strategies based on an integral sliding mode control (ISMC) technique are proposed to maintain a rigid formation for a string of vehicle platoon in one dimension. First, distributed adaptive control based on traditional constant time headway (TCTH) policy under the assumption that the initial spacing and velocity errors are zero is developed to guarantee that all spacing errors are uniformly ultimately bounded and that the string stability of the whole vehicle platoon is also satisfied. Then, a modified constant time headway (MCTH) policy is proposed to remove the assumption of zero initial spacing and velocity errors and simultaneously effectively decrease the intervehicle spacing (i.e., increase the traffic density), making them nearly equal to those by using the constant spacing (CS) policy. Adaptive compensation terms without requiring a prior knowledge of upper bounds of the uncertainties are constructed to compensate for the time-variant effects caused by nonlinear acceleration uncertainties. Finally, numerical simulation results show the validity and advantages of the proposed policy up to a significant higher traffic density.

With the increasing demand of spectrum and emergence of mobile devices, a cloud radio access network (C-RAN) is a promising technology to improve network capacity and coverage. The key concept of the C-RAN is to separate the radio function unit in remote radio heads (RRHs) from the digital function unit in baseband units (BBUs). This separation facilitates efficient spectrum and infrastructure sharing in the C-RAN. This paper considers the cooperative interference management among RRHs in the C-RAN. The RRHs can form coalitions to mitigate intra-interference and jointly serve their subscribers. To characterize RRH performance under the coalition, this paper develops two signal-to-interference-plus-noise-ratio (SINR)-based downlink throughput models of RRH, where one adopts tools from stochastic geometry to capture the interference from vehicular users (VUs) belonging to noncooperative RRHs, and another ignores the VU interference. In this paper, a coalition formation game is formulated to model the situation in which RRHs of C-RAN can make an individual decision to cooperatively serve their VUs if the throughput of the RRH can be improved. To obtain the solution of the proposed game, we develop a distributed coalition formation algorithm and analyze the stability of the coalition structure using a Markov chain model. Simulation results show that, as compared with the noncoalition and grand coalition, our distributed coalition formation can improve the C-RAN throughput by at least 20% and 80%, respectively. Furthermore, according to extensive simulation, we can define the conditions needed for RRHs to form a coalition and obtain higher throughput.

In cognitive radio networks, secondary users (SUs) can use a frequency band when the primary user (PU) of the band is not transmitting. Spectrum sensing is one of the methods that SUs employ to know if the PU has stopped transmitting. Accurate spectrum sensing boosts the likelihood that an SU detects a spectrum opportunity. However, it will be challenging for affordable receivers to support accurate spectrum sensing due to receiver impairments, including IQ imbalance and aliasing. Several other papers have developed methods that can address the effects of IQ imbalance in spectrum sensing. This paper presents a technique that can address not only IQ imbalance but the effects of aliasing on multiband spectrum sensing as well. We use channelized spectrum-domain modeling of receivers to develop the technique. This paper presents the theoretical development of the receiver modeling technique and the proposed spectrum sensing mechanism. The performance of the proposed mechanism is evaluated using simulations and hardware-based measurements. The results demonstrate that the proposed mechanism significantly enhances the opportunity detection probability of affordable cognitive radios with high IQ imbalance and poor antialiasing filter selectivity performances.

This work considers the problem of reliability and secrecy enhancement for wireless content sharing between a content source node and a content requester. By exploiting the social characteristics of multiple nodes in the presence of multiple independent eavesdroppers, this paper first investigates the impact of mobility for source node selection for transmission reliability, within a cooperative wireless network. Furthermore, trustworthiness and social-tie-based jammer node selections are explored for cooperative jamming to provide secrecy, while allocating power appropriately to the source node and the cooperative jammer node to maximize the worst-case ergodic secrecy rate. Specifically, the source transmitter is selected by first narrowing the potential link set to provide the desired transmission success rate while considering the physical channel and social characteristics jointly. Moreover, the effect of jammer dependability based on its social interactive behavior is also investigated. Instead of focusing on full channel state information (CSI), this paper also investigates the more practical case in which only statistical CSI knowledge is known. Our optimization is based on the upper and lower bounds to the secrecy rate to simplify the power optimization problem without noticeable performance loss. Additional performance analysis for the impact of social characteristics is given. Finally, a one-dimensional search is proposed to further reduce the computational complexity of the power optimization.

The Internet Protocol (IP) Multimedia Subsystem (IMS) is a framework for delivering all-IP-based services. Voice via IMS has been defined as a possible solution from Third-Generation Partnership Project (3GPP) Release 5, prior to long-term evolution (LTE). However, the cost and immediate need for IMS services prevented operators from migration to IMS-based services, particularly with the wide presence of circuit-switched (CS) services offered in Global System for Mobile Communications (GSM) and Universal Mobile Telecommunications System (UMTS) networks. CS fallback (CSFB), which was introduced in 3GPP Release 8, enables the support of voice service without IMS. This is the common deployed scenario for many exiting LTE networks. Voice over LTE (VoLTE) is compulsory to offer rich communication services (RCS) via IMS, in addition to improving the performance of the already deployed CSFB voice solution. However, CSFB and VoLTE are still deployed concurrently, where operators can gradually roll out an LTE/IMS system, while still supporting 2G/3G fallback mechanism. It is, therefore, important to benchmark the performance of both solutions, highlight the deployment challenges, and study the impacts on the end-user experience. This paper provides performance analysis, deployment challenges, and comparisons between these voice solutions. This paper presents practical performance analysis, including end-to-end assessment of call setup delay under different radio conditions, main challenges impacting the in-call performance, as well as performance aspects of Single Radio Voice Call Continuity (SRVCC) and its evolution releases.

Functional safety is of great importance for electric and hybrid electric vehicles (EV/HEVs). One way to improve functional safety of EV/HEVs is to develop reliable and robust onboard diagnostics (OBD) so that, once a component fault is detected, effective remedial actions are taken to avoid system failures. In this paper, we develop a systematic model-based diagnostic approach based on structural analysis for electric drive systems that can form the basis of OBD design for EVs. The structural analysis approach for fault detection and isolation (FDI) evaluates a system's structural model, using the mathematical model of the system in matrix form, from which it is possible to determine the analytic redundancy and to design the structured residuals. In this paper, we demonstrate the application of this methodology by carrying out the design of a diagnostic approach for permanent-magnet synchronous machine (PMSM) drive systems of EVs, with specific focus on sensor fault diagnosis. The diagnostic approach is first verified through a simulation study on the EcoCAR2 vehicle, which is a plug-in HEV developed by the Center for Automotive Research at The Ohio State University, and is then further validated through an experimental study on a test bench consisting of a three-phase inverter enabled by the TMS320F28035 digital signal processor and a PMSM.